4d443b9228
### What this PR does / why we need it?
This PR restores #7029, which adds W8A8C8 support for dsv3.2/glm5 using
the `lightning_indexer_quant` ops in the pd-mix stage.
The original PR was reverted by #7288 because the patch did not work
with the recompute scheduler.
This PR also fixes the patching issue so that it works correctly with
the recompute scheduler.
### Does this PR introduce _any_ user-facing change?
Yes. To enable LI C8, users need to set the `enable_sparse_c8` option to
`"true"` in `additional_config`.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
1025 lines
50 KiB
Python
1025 lines
50 KiB
Python
##
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/vllm/v1/core/sched/scheduler.py
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#
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from __future__ import annotations
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import time
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from collections import defaultdict, deque
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from dataclasses import dataclass, fields
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from vllm.config import SchedulerConfig, VllmConfig
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from vllm.distributed.ec_transfer.ec_connector.base import ECConnectorMetadata
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from vllm.distributed.kv_events import KVEventBatch
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from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
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from vllm.distributed.kv_transfer.kv_connector.v1.metrics import KVConnectorStats
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from vllm.logger import init_logger
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from vllm.v1.core.kv_cache_manager import KVCacheBlocks
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from vllm.v1.core.sched.async_scheduler import AsyncScheduler
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from vllm.v1.core.sched.output import NewRequestData, SchedulerOutput
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from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
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from vllm.v1.core.sched.scheduler import Scheduler
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from vllm.v1.core.sched.utils import remove_all
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from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs, FinishReason
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from vllm.v1.metrics.perf import PerfStats
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from vllm.v1.outputs import ModelRunnerOutput
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from vllm.v1.request import Request, RequestStatus, StreamingUpdate
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from vllm.v1.sample.rejection_sampler import PLACEHOLDER_TOKEN_ID
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from vllm.v1.spec_decode.metrics import SpecDecodingStats
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from vllm.v1.utils import ConstantList, record_function_or_nullcontext
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logger = init_logger(__name__)
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# `spec_manager_map` in single_type_kv_cache_manager is a module-level dict
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# whose keys are class objects bound at import time. When the async
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# recompute scheduler is enabled, `recompute_scheduler.py` is imported by
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# `check_and_update_config()` (via AsyncScheduler → scheduler.py →
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# kv_cache_coordinator → single_type_kv_cache_manager) *before*
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# this patch file is executed a second time (e.g. triggered by
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# unpickling an AscendMLAAttentionSpec in the EngineCoreProc subprocess).
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# In that case the dict already contains the original MLAAttentionSpec
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# class as a key, so a subsequent lookup with type(AscendMLAAttentionSpec
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# instance) raises KeyError.
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#
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# Fix: whenever this patch is applied, register AscendMLAAttentionSpec as
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# an additional key in spec_manager_map (if the module is already loaded).
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def register_ascend_mla_spec_in_manager():
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import sys as _sys
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from vllm.v1.core.single_type_kv_cache_manager import FullAttentionManager
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from vllm.v1.kv_cache_interface import MLAAttentionSpec as AscendMLAAttentionSpec
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_stm = _sys.modules.get("vllm.v1.core.single_type_kv_cache_manager")
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if _stm is not None and AscendMLAAttentionSpec not in _stm.spec_manager_map:
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_stm.spec_manager_map[AscendMLAAttentionSpec] = FullAttentionManager
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@dataclass
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class RecomputeSchedulerConfig(SchedulerConfig):
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scheduler_cls: str | type[object] = "vllm_ascend.core.recompute_scheduler.RecomputeScheduler"
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@classmethod
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def initialize_from_config(cls, vllm_config: VllmConfig):
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vllm_scheduler_config = vllm_config.scheduler_config
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scheduler_config = {
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field.name: getattr(vllm_scheduler_config, field.name)
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for field in fields(vllm_scheduler_config)
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if field.init
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}
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if vllm_scheduler_config.async_scheduling:
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scheduler_config["scheduler_cls"] = "vllm_ascend.core.recompute_scheduler.AsyncRecomputeScheduler"
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else:
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scheduler_config["scheduler_cls"] = "vllm_ascend.core.recompute_scheduler.RecomputeScheduler"
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scheduler_config["max_model_len"] = vllm_config.model_config.max_model_len
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scheduler_config["is_encoder_decoder"] = vllm_config.model_config.is_encoder_decoder
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return cls(**scheduler_config)
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@dataclass
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class RecomputeReqInfo:
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request_id: str
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output_token_ids: ConstantList
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client_index: int = 0
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@dataclass
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class RecomputeSchedulerOutput(SchedulerOutput):
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recomputed_reqs: list[RecomputeReqInfo] | None = None
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class RecomputeScheduler(Scheduler):
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running: list[Request]
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def __init__(self, *args, **kwargs):
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register_ascend_mla_spec_in_manager()
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super().__init__(*args, **kwargs)
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# When is_mtp_kv_consumer is true, we will fill request.spec_token_ids
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# with placeholder tokens to enable full graph when decode nodes pull
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# the KV cache of one request from prefill nodes.
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self.is_mtp_kv_consumer = (
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self.vllm_config.speculative_config
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and self.vllm_config.kv_transfer_config
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and self.vllm_config.kv_transfer_config.is_kv_consumer
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)
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self.is_kv_producer = self.vllm_config.kv_transfer_config and self.vllm_config.kv_transfer_config.is_kv_producer
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self.is_hybrid_model = (
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"qwen3_next" in self.vllm_config.model_config.hf_text_config.model_type
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or "qwen3_5" in self.vllm_config.model_config.hf_text_config.model_type
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)
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def add_request(self, request: Request) -> None:
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existing = self.requests.get(request.request_id)
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if existing is not None:
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update = StreamingUpdate.from_request(request)
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if existing.status != RequestStatus.WAITING_FOR_STREAMING_REQ:
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assert existing.streaming_queue is not None, "duplicate request id"
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# Queue next input chunk (or finished sentinel).
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existing.streaming_queue.append(update)
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elif update is not None:
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# Commence next input chunk.
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self._update_request_as_session(existing, update)
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else:
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# Streaming-input session finished.
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self.finish_requests(request.request_id, RequestStatus.FINISHED_ABORTED)
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else:
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if request.resumable:
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request.streaming_queue = deque()
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# Fill in placeholder tokens to enable full graph compatibility. Without
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# placeholders, graph matching may fail, forcing eager mode execution.
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if self.is_kv_producer and self.is_hybrid_model and request.num_tokens > 1:
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request.prompt_token_ids.pop()
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request._all_token_ids.pop()
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request.num_prompt_tokens -= 1
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if self.is_mtp_kv_consumer:
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request.spec_token_ids = [PLACEHOLDER_TOKEN_ID] * self.num_spec_tokens
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self.waiting.add_request(request)
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self.requests[request.request_id] = request
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if self.log_stats:
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request.record_event(EngineCoreEventType.QUEUED)
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def _update_waiting_for_remote_kv(self, request: Request) -> bool:
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"""
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KV Connector: check if the request_id is finished_recving.
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The finished_recving_kv_req_ids list is populated
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on the previous steps()'s update_from_output based
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on the worker side connector.
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When the kv transfer is ready, we cache the blocks
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and the request state will be moved back to WAITING from
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WAITING_FOR_REMOTE_KV.
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"""
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assert self.connector is not None
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if request.request_id not in self.finished_recving_kv_req_ids:
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return False
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if request.request_id in self.failed_recving_kv_req_ids:
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# Request had KV load failures; num_computed_tokens was already
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# updated in _update_requests_with_invalid_blocks
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if request.num_computed_tokens:
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# Cache any valid computed tokens.
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self.kv_cache_manager.cache_blocks(request, request.num_computed_tokens)
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else:
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# No valid computed tokens, release allocated blocks.
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# There may be a local cache hit on retry.
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self.kv_cache_manager.free(request)
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self.failed_recving_kv_req_ids.remove(request.request_id)
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else:
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# Now that the blocks are ready, actually cache them.
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block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
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if len(block_ids) == 1:
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num_computed_tokens = len(block_ids[0]) * self.block_size
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# Handle the case where num request tokens less than one block.
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num_computed_tokens = min(num_computed_tokens, request.num_tokens)
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else:
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num_computed_tokens = request.num_tokens
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if num_computed_tokens == request.num_tokens:
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num_computed_tokens -= 1
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# This will cache the blocks iff caching is enabled.
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self.kv_cache_manager.cache_blocks(request, num_computed_tokens)
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# Update the request state for scheduling.
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request.num_computed_tokens = num_computed_tokens
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# Return that we are ready.
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self.finished_recving_kv_req_ids.remove(request.request_id)
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return True
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def schedule(self) -> RecomputeSchedulerOutput:
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# NOTE(woosuk) on the scheduling algorithm:
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# There's no "decoding phase" nor "prefill phase" in the scheduler.
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# Each request just has the num_computed_tokens and
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# num_tokens_with_spec. num_tokens_with_spec =
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# len(prompt_token_ids) + len(output_token_ids) + len(spec_token_ids).
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# At each step, the scheduler tries to assign tokens to the requests
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# so that each request's num_computed_tokens can catch up its
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# num_tokens_with_spec. This is general enough to cover
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# chunked prefills, prefix caching, speculative decoding,
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# and the "jump decoding" optimization in the future.
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scheduled_new_reqs: list[Request] = []
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scheduled_resumed_reqs: list[Request] = []
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scheduled_running_reqs: list[Request] = []
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preempted_reqs: list[Request] = []
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recomputed_reqs: list[RecomputeReqInfo] = []
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req_to_new_blocks: dict[str, KVCacheBlocks] = {}
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num_scheduled_tokens: dict[str, int] = {}
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token_budget = self.max_num_scheduled_tokens
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# Encoder-related.
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scheduled_encoder_inputs: dict[str, list[int]] = {}
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encoder_compute_budget = self.max_num_encoder_input_tokens
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# Spec decode-related.
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scheduled_spec_decode_tokens: dict[str, list[int]] = {}
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# For logging.
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scheduled_timestamp = time.monotonic()
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# First, schedule the RUNNING requests.
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req_index = 0
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while req_index < len(self.running) and token_budget > 0:
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request = self.running[req_index]
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if (
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request.num_output_placeholders > 0
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# This is (num_computed_tokens + 1) - (num_output_placeholders - 1).
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# Since output placeholders are also included in the computed tokens
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# count, we subtract (num_output_placeholders - 1) to remove any draft
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# tokens, so that we can be sure no further steps are needed even if
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# they are all rejected.
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and request.num_computed_tokens + 2 - request.num_output_placeholders
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>= request.num_prompt_tokens + request.max_tokens
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):
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# Async scheduling: Avoid scheduling an extra step when we are sure that
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# the previous step has reached request.max_tokens. We don't schedule
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# partial draft tokens since this prevents uniform decode optimizations.
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req_index += 1
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continue
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num_new_tokens = (
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request.num_tokens_with_spec + request.num_output_placeholders - request.num_computed_tokens
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)
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if 0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens:
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num_new_tokens = self.scheduler_config.long_prefill_token_threshold
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num_new_tokens = min(num_new_tokens, token_budget)
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# Make sure the input position does not exceed the max model len.
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# This is necessary when using spec decoding.
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num_new_tokens = min(num_new_tokens, self.max_model_len - 1 - request.num_computed_tokens)
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# Schedule encoder inputs.
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encoder_inputs_to_schedule = None
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external_load_encoder_input: list[int] = []
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new_encoder_compute_budget = encoder_compute_budget
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if request.has_encoder_inputs:
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(
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encoder_inputs_to_schedule,
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num_new_tokens,
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new_encoder_compute_budget,
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external_load_encoder_input,
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) = self._try_schedule_encoder_inputs(
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request,
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request.num_computed_tokens,
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num_new_tokens,
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encoder_compute_budget,
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shift_computed_tokens=1 if self.use_eagle else 0,
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)
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if self.need_mamba_block_aligned_split:
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num_new_tokens = self._mamba_block_aligned_split(request, num_new_tokens)
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if num_new_tokens == 0:
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# The request cannot be scheduled because one of the following
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# reasons:
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# 1. No new tokens to schedule. This may happen when
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# (1) PP>1 and we have already scheduled all prompt tokens
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# but they are not finished yet.
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# (2) Async scheduling and the request has reached to either
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# its max_total_tokens or max_model_len.
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# 2. The encoder budget is exhausted.
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# 3. The encoder cache is exhausted.
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# 4. Insufficient budget for a block-aligned chunk in hybrid
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# models with mamba cache mode \"align\".
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# NOTE(woosuk): Here, by doing `continue` instead of `break`,
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# we do not strictly follow the FCFS scheduling policy and
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# allow the lower-priority requests to be scheduled.
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req_index += 1
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continue
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# Schedule newly needed KV blocks for the request.
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with record_function_or_nullcontext("schedule: allocate_slots"):
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while True:
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new_blocks = self.kv_cache_manager.allocate_slots(
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request,
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num_new_tokens,
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num_lookahead_tokens=self.num_lookahead_tokens,
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)
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if new_blocks is not None:
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# The request can be scheduled.
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break
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# The request cannot be scheduled.
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# Preempt the lowest-priority request.
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# NOTE: We add the preempted_req to recomputed_reqs in kv_consumer to
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# drop the request to PD proxy.
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transfer_config = self.vllm_config.kv_transfer_config
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if transfer_config is not None and not transfer_config.is_kv_producer:
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recomputed_req = self.running.pop()
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self.kv_cache_manager.free(recomputed_req)
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recomputed_reqs.append(
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RecomputeReqInfo(
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recomputed_req.request_id, recomputed_req.output_token_ids, recomputed_req.client_index
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)
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)
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if recomputed_req == request:
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break
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else:
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if self.policy == SchedulingPolicy.PRIORITY:
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preempted_req = max(
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self.running,
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key=lambda r: (r.priority, r.arrival_time),
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)
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self.running.remove(preempted_req)
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if preempted_req in scheduled_running_reqs:
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preempted_req_id = preempted_req.request_id
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scheduled_running_reqs.remove(preempted_req)
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token_budget += num_scheduled_tokens.pop(preempted_req_id)
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req_to_new_blocks.pop(preempted_req_id)
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scheduled_spec_decode_tokens.pop(preempted_req_id, None)
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preempted_encoder_inputs = scheduled_encoder_inputs.pop(preempted_req_id, None)
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if preempted_encoder_inputs:
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# Restore encoder compute budget if the preempted
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# request had encoder inputs scheduled in this step.
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num_embeds_to_restore = sum(
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preempted_req.get_num_encoder_embeds(i) for i in preempted_encoder_inputs
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)
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encoder_compute_budget += num_embeds_to_restore
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req_index -= 1
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else:
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preempted_req = self.running.pop()
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self._preempt_request(preempted_req, scheduled_timestamp)
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preempted_reqs.append(preempted_req)
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if preempted_req == request:
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# No more request to preempt. Cannot schedule this request.
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break
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if new_blocks is None:
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# Cannot schedule this request.
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break
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# Schedule the request.
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scheduled_running_reqs.append(request)
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request_id = request.request_id
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req_to_new_blocks[request_id] = new_blocks
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num_scheduled_tokens[request_id] = num_new_tokens
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token_budget -= num_new_tokens
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req_index += 1
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# Speculative decode related.
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if request.spec_token_ids:
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num_scheduled_spec_tokens = (
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num_new_tokens + request.num_computed_tokens - request.num_tokens - request.num_output_placeholders
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)
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if num_scheduled_spec_tokens > 0:
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spec_token_ids = request.spec_token_ids
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if len(spec_token_ids) > num_scheduled_spec_tokens:
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spec_token_ids = spec_token_ids[:num_scheduled_spec_tokens]
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scheduled_spec_decode_tokens[request.request_id] = spec_token_ids
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# New spec tokens will be set in `update_draft_token_ids` before the
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# next step when applicable.
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request.spec_token_ids = []
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# Encoder-related.
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if encoder_inputs_to_schedule:
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scheduled_encoder_inputs[request_id] = encoder_inputs_to_schedule
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# Allocate the encoder cache.
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for i in encoder_inputs_to_schedule:
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self.encoder_cache_manager.allocate(request, i)
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encoder_compute_budget = new_encoder_compute_budget
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if external_load_encoder_input:
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for i in external_load_encoder_input:
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self.encoder_cache_manager.allocate(request, i)
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if self.ec_connector is not None:
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self.ec_connector.update_state_after_alloc(request, i)
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# Record the LoRAs in scheduled_running_reqs
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scheduled_loras: set[int] = set()
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if self.lora_config:
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scheduled_loras = set(
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req.lora_request.lora_int_id
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for req in scheduled_running_reqs
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if req.lora_request and req.lora_request.lora_int_id > 0
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)
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assert len(scheduled_loras) <= self.lora_config.max_loras
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# Use a temporary RequestQueue to collect requests that need to be
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# skipped and put back at the head of the waiting queue later
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skipped_waiting_requests = create_request_queue(self.policy)
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|
|
# Next, schedule the WAITING requests.
|
|
if not preempted_reqs and not recomputed_reqs:
|
|
while self.waiting and token_budget > 0:
|
|
if len(self.running) == self.max_num_running_reqs:
|
|
break
|
|
|
|
request = self.waiting.peek_request()
|
|
request_id = request.request_id
|
|
|
|
# KVTransfer: skip request if still waiting for remote kvs.
|
|
if request.status == RequestStatus.WAITING_FOR_REMOTE_KVS:
|
|
is_ready = self._update_waiting_for_remote_kv(request)
|
|
if is_ready:
|
|
if request.num_preemptions:
|
|
# We must be loading for a resumed preemption
|
|
# rather than a new request.
|
|
request.status = RequestStatus.PREEMPTED
|
|
else:
|
|
request.status = RequestStatus.WAITING
|
|
else:
|
|
logger.debug(
|
|
"%s is still in WAITING_FOR_REMOTE_KVS state.",
|
|
request_id,
|
|
)
|
|
self.waiting.pop_request()
|
|
skipped_waiting_requests.prepend_request(request)
|
|
continue
|
|
|
|
# Skip request if the structured output request is still waiting
|
|
# for FSM compilation.
|
|
if request.status == RequestStatus.WAITING_FOR_FSM:
|
|
structured_output_req = request.structured_output_request
|
|
if structured_output_req and structured_output_req.grammar:
|
|
request.status = RequestStatus.WAITING
|
|
else:
|
|
self.waiting.pop_request()
|
|
skipped_waiting_requests.prepend_request(request)
|
|
continue
|
|
|
|
# Streaming: skip request if still waiting for next streaming req.
|
|
if request.status == RequestStatus.WAITING_FOR_STREAMING_REQ:
|
|
assert not request.streaming_queue
|
|
self.waiting.pop_request()
|
|
skipped_waiting_requests.prepend_request(request)
|
|
continue
|
|
|
|
# Check that adding the request still respects the max_loras
|
|
# constraint.
|
|
if (
|
|
self.lora_config
|
|
and request.lora_request
|
|
and (
|
|
len(scheduled_loras) == self.lora_config.max_loras
|
|
and request.lora_request.lora_int_id not in scheduled_loras
|
|
)
|
|
):
|
|
# Scheduling would exceed max_loras, skip.
|
|
self.waiting.pop_request()
|
|
skipped_waiting_requests.prepend_request(request)
|
|
continue
|
|
|
|
num_external_computed_tokens = 0
|
|
load_kv_async = False
|
|
connector_prefix_cache_queries, connector_prefix_cache_hits = 0, 0
|
|
|
|
# Get already-cached tokens.
|
|
if request.num_computed_tokens == 0:
|
|
# Get locally-cached tokens.
|
|
new_computed_blocks, num_new_local_computed_tokens = self.kv_cache_manager.get_computed_blocks(
|
|
request
|
|
)
|
|
|
|
# Get externally-cached tokens if using a KVConnector.
|
|
if self.connector is not None:
|
|
ext_tokens, load_kv_async = self.connector.get_num_new_matched_tokens(
|
|
request, num_new_local_computed_tokens
|
|
)
|
|
|
|
if ext_tokens is None:
|
|
# The request cannot be scheduled because
|
|
# the KVConnector couldn't determine
|
|
# the number of matched tokens.
|
|
self.waiting.pop_request()
|
|
skipped_waiting_requests.prepend_request(request)
|
|
continue
|
|
|
|
request.num_external_computed_tokens = ext_tokens
|
|
num_external_computed_tokens = ext_tokens
|
|
|
|
connector_prefix_cache_queries = request.num_tokens - num_new_local_computed_tokens
|
|
connector_prefix_cache_hits = num_external_computed_tokens
|
|
|
|
# Total computed tokens (local + external).
|
|
num_computed_tokens = num_new_local_computed_tokens + num_external_computed_tokens
|
|
else:
|
|
# KVTransfer: WAITING reqs have num_computed_tokens > 0
|
|
# after async KV recvs are completed.
|
|
new_computed_blocks = self.kv_cache_manager.empty_kv_cache_blocks
|
|
num_new_local_computed_tokens = 0
|
|
num_computed_tokens = request.num_computed_tokens
|
|
|
|
encoder_inputs_to_schedule = None
|
|
external_load_encoder_input = []
|
|
new_encoder_compute_budget = encoder_compute_budget
|
|
|
|
if load_kv_async:
|
|
# KVTransfer: loading remote KV, do not allocate for new work.
|
|
assert num_external_computed_tokens > 0
|
|
num_new_tokens = 0
|
|
else:
|
|
# Number of tokens to be scheduled.
|
|
# We use `request.num_tokens` instead of
|
|
# `request.num_prompt_tokens` to consider the resumed
|
|
# requests, which have output tokens.
|
|
if self.is_mtp_kv_consumer:
|
|
num_new_tokens = request.num_tokens_with_spec - num_computed_tokens
|
|
else:
|
|
num_new_tokens = request.num_tokens - num_computed_tokens
|
|
threshold = self.scheduler_config.long_prefill_token_threshold
|
|
if 0 < threshold < num_new_tokens:
|
|
num_new_tokens = threshold
|
|
|
|
# chunked prefill has to be enabled explicitly to allow
|
|
# pooling requests to be chunked
|
|
if not self.scheduler_config.enable_chunked_prefill and num_new_tokens > token_budget:
|
|
# If chunked_prefill is disabled,
|
|
# we can stop the scheduling here.
|
|
break
|
|
|
|
num_new_tokens = min(num_new_tokens, token_budget)
|
|
assert num_new_tokens > 0
|
|
|
|
# Schedule encoder inputs.
|
|
if request.has_encoder_inputs:
|
|
(
|
|
encoder_inputs_to_schedule,
|
|
num_new_tokens,
|
|
new_encoder_compute_budget,
|
|
external_load_encoder_input,
|
|
) = self._try_schedule_encoder_inputs(
|
|
request,
|
|
num_computed_tokens,
|
|
num_new_tokens,
|
|
encoder_compute_budget,
|
|
shift_computed_tokens=1 if self.use_eagle else 0,
|
|
)
|
|
if num_new_tokens == 0:
|
|
# The request cannot be scheduled.
|
|
break
|
|
|
|
if self.need_mamba_block_aligned_split:
|
|
num_new_tokens = self._mamba_block_aligned_split(
|
|
request,
|
|
num_new_tokens,
|
|
num_new_local_computed_tokens,
|
|
num_external_computed_tokens,
|
|
)
|
|
if num_new_tokens == 0:
|
|
break
|
|
|
|
# Handles an edge case when P/D Disaggregation
|
|
# is used with Spec Decoding where an
|
|
# extra block gets allocated which
|
|
# creates a mismatch between the number
|
|
# of local and remote blocks.
|
|
effective_lookahead_tokens = 0 if request.num_computed_tokens == 0 else self.num_lookahead_tokens
|
|
|
|
num_encoder_tokens = (
|
|
self._num_encoder_max_input_tokens if self.is_encoder_decoder and request.has_encoder_inputs else 0
|
|
)
|
|
|
|
new_blocks = self.kv_cache_manager.allocate_slots(
|
|
request,
|
|
num_new_tokens,
|
|
num_new_computed_tokens=num_new_local_computed_tokens,
|
|
new_computed_blocks=new_computed_blocks,
|
|
num_lookahead_tokens=effective_lookahead_tokens,
|
|
num_external_computed_tokens=num_external_computed_tokens,
|
|
delay_cache_blocks=load_kv_async,
|
|
num_encoder_tokens=num_encoder_tokens,
|
|
)
|
|
|
|
if new_blocks is None:
|
|
# The request cannot be scheduled.
|
|
|
|
# NOTE: we need to untouch the request from the encode cache
|
|
# manager
|
|
if request.has_encoder_inputs:
|
|
self.encoder_cache_manager.free(request)
|
|
break
|
|
|
|
# KVTransfer: the connector uses this info to determine
|
|
# if a load is needed. Note that
|
|
# This information is used to determine if a load is
|
|
# needed for this request.
|
|
if self.connector is not None:
|
|
self.connector.update_state_after_alloc(
|
|
request,
|
|
self.kv_cache_manager.get_blocks(request_id),
|
|
num_external_computed_tokens,
|
|
)
|
|
if self.connector_prefix_cache_stats is not None and connector_prefix_cache_queries != 0:
|
|
self.connector_prefix_cache_stats.record(
|
|
num_tokens=connector_prefix_cache_queries,
|
|
num_hits=connector_prefix_cache_hits,
|
|
preempted=request.num_preemptions > 0,
|
|
)
|
|
|
|
# Request was already popped from self.waiting
|
|
# unless it was re-added above due to new_blocks being None.
|
|
request = self.waiting.pop_request()
|
|
if load_kv_async:
|
|
# If loading async, allocate memory and put request
|
|
# into the WAITING_FOR_REMOTE_KV state.
|
|
skipped_waiting_requests.prepend_request(request)
|
|
request.status = RequestStatus.WAITING_FOR_REMOTE_KVS
|
|
continue
|
|
|
|
# For spec_token_ids, the waiting queue has the same processing
|
|
# as the running queue.
|
|
if self.is_mtp_kv_consumer and request.spec_token_ids:
|
|
num_scheduled_spec_tokens = (
|
|
num_new_tokens
|
|
+ request.num_computed_tokens
|
|
- request.num_tokens
|
|
- request.num_output_placeholders
|
|
)
|
|
if num_scheduled_spec_tokens > 0:
|
|
spec_token_ids = request.spec_token_ids
|
|
if len(spec_token_ids) > num_scheduled_spec_tokens:
|
|
spec_token_ids = spec_token_ids[:num_scheduled_spec_tokens]
|
|
scheduled_spec_decode_tokens[request.request_id] = spec_token_ids
|
|
|
|
# New spec tokens will be set in `update_draft_token_ids` before the
|
|
# next step when applicable.
|
|
request.spec_token_ids = []
|
|
|
|
self.running.append(request)
|
|
if self.log_stats:
|
|
request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp)
|
|
if request.status == RequestStatus.WAITING:
|
|
scheduled_new_reqs.append(request)
|
|
elif request.status == RequestStatus.PREEMPTED:
|
|
scheduled_resumed_reqs.append(request)
|
|
else:
|
|
raise RuntimeError(f"Invalid request status: {request.status}")
|
|
|
|
if self.lora_config and request.lora_request:
|
|
scheduled_loras.add(request.lora_request.lora_int_id)
|
|
req_to_new_blocks[request_id] = self.kv_cache_manager.get_blocks(request_id)
|
|
num_scheduled_tokens[request_id] = num_new_tokens
|
|
token_budget -= num_new_tokens
|
|
request.status = RequestStatus.RUNNING
|
|
request.num_computed_tokens = num_computed_tokens
|
|
# Count the number of prefix cached tokens.
|
|
if request.num_cached_tokens < 0:
|
|
request.num_cached_tokens = num_computed_tokens
|
|
# Encoder-related.
|
|
if encoder_inputs_to_schedule:
|
|
scheduled_encoder_inputs[request_id] = encoder_inputs_to_schedule
|
|
# Allocate the encoder cache.
|
|
for i in encoder_inputs_to_schedule:
|
|
self.encoder_cache_manager.allocate(request, i)
|
|
encoder_compute_budget = new_encoder_compute_budget
|
|
# Allocate for external load encoder cache
|
|
if external_load_encoder_input:
|
|
for i in external_load_encoder_input:
|
|
self.encoder_cache_manager.allocate(request, i)
|
|
if self.ec_connector is not None:
|
|
self.ec_connector.update_state_after_alloc(request, i)
|
|
# Put back any skipped requests at the head of the waiting queue
|
|
if skipped_waiting_requests:
|
|
self.waiting.prepend_requests(skipped_waiting_requests)
|
|
|
|
# Check if the scheduling constraints are satisfied.
|
|
total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
|
|
assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens
|
|
|
|
assert token_budget >= 0
|
|
assert len(self.running) <= self.max_num_running_reqs
|
|
# Since some requests in the RUNNING queue may not be scheduled in
|
|
# this step, the total number of scheduled requests can be smaller than
|
|
# len(self.running).
|
|
assert len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + len(scheduled_running_reqs) <= len(self.running)
|
|
|
|
# Get the longest common prefix among all requests in the running queue.
|
|
# This can be potentially used for cascade attention.
|
|
num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
|
|
with record_function_or_nullcontext("schedule: get_num_common_prefix_blocks"):
|
|
if self.running:
|
|
any_request_id = self.running[0].request_id
|
|
num_common_prefix_blocks = self.kv_cache_manager.get_num_common_prefix_blocks(any_request_id)
|
|
|
|
# Construct the scheduler output.
|
|
if self.use_v2_model_runner:
|
|
scheduled_new_reqs = scheduled_new_reqs + scheduled_resumed_reqs
|
|
scheduled_resumed_reqs = []
|
|
new_reqs_data = [
|
|
NewRequestData.from_request(
|
|
req,
|
|
req_to_new_blocks[req.request_id].get_block_ids(),
|
|
req._all_token_ids,
|
|
)
|
|
for req in scheduled_new_reqs
|
|
]
|
|
else:
|
|
new_reqs_data = [
|
|
NewRequestData.from_request(req, req_to_new_blocks[req.request_id].get_block_ids())
|
|
for req in scheduled_new_reqs
|
|
]
|
|
|
|
with record_function_or_nullcontext("schedule: make_cached_request_data"):
|
|
cached_reqs_data = self._make_cached_request_data(
|
|
scheduled_running_reqs,
|
|
scheduled_resumed_reqs,
|
|
num_scheduled_tokens,
|
|
scheduled_spec_decode_tokens,
|
|
req_to_new_blocks,
|
|
)
|
|
|
|
# Record the request ids that were scheduled in this step.
|
|
self.prev_step_scheduled_req_ids.clear()
|
|
self.prev_step_scheduled_req_ids.update(num_scheduled_tokens.keys())
|
|
|
|
scheduler_output = RecomputeSchedulerOutput(
|
|
scheduled_new_reqs=new_reqs_data,
|
|
scheduled_cached_reqs=cached_reqs_data,
|
|
num_scheduled_tokens=num_scheduled_tokens,
|
|
total_num_scheduled_tokens=total_num_scheduled_tokens,
|
|
scheduled_spec_decode_tokens=scheduled_spec_decode_tokens,
|
|
scheduled_encoder_inputs=scheduled_encoder_inputs,
|
|
num_common_prefix_blocks=num_common_prefix_blocks,
|
|
preempted_req_ids={req.request_id for req in preempted_reqs},
|
|
# finished_req_ids is an existing state in the scheduler,
|
|
# instead of being newly scheduled in this step.
|
|
# It contains the request IDs that are finished in between
|
|
# the previous and the current steps.
|
|
finished_req_ids=self.finished_req_ids,
|
|
free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(),
|
|
recomputed_reqs=recomputed_reqs,
|
|
)
|
|
|
|
# NOTE(Kuntai): this function is designed for multiple purposes:
|
|
# 1. Plan the KV cache store
|
|
# 2. Wrap up all the KV cache load / save ops into an opaque object
|
|
# 3. Clear the internal states of the connector
|
|
if self.connector is not None:
|
|
meta: KVConnectorMetadata = self.connector.build_connector_meta(scheduler_output)
|
|
scheduler_output.kv_connector_metadata = meta
|
|
|
|
# Build the connector meta for ECConnector
|
|
if self.ec_connector is not None:
|
|
ec_meta: ECConnectorMetadata = self.ec_connector.build_connector_meta(scheduler_output)
|
|
scheduler_output.ec_connector_metadata = ec_meta
|
|
|
|
with record_function_or_nullcontext("schedule: update_after_schedule"):
|
|
self._update_after_schedule(scheduler_output)
|
|
return scheduler_output
|
|
|
|
def update_from_output(
|
|
self,
|
|
scheduler_output: SchedulerOutput,
|
|
model_runner_output: ModelRunnerOutput,
|
|
) -> dict[int, EngineCoreOutputs]:
|
|
sampled_token_ids = model_runner_output.sampled_token_ids
|
|
logprobs = model_runner_output.logprobs
|
|
prompt_logprobs_dict = model_runner_output.prompt_logprobs_dict
|
|
num_scheduled_tokens = scheduler_output.num_scheduled_tokens
|
|
pooler_outputs = model_runner_output.pooler_output
|
|
num_nans_in_logits = model_runner_output.num_nans_in_logits
|
|
kv_connector_output = model_runner_output.kv_connector_output
|
|
cudagraph_stats = model_runner_output.cudagraph_stats
|
|
|
|
perf_stats: PerfStats | None = None
|
|
if self.perf_metrics and self.perf_metrics.is_enabled():
|
|
perf_stats = self.perf_metrics.get_step_perf_stats_per_gpu(scheduler_output)
|
|
|
|
outputs: dict[int, list[EngineCoreOutput]] = defaultdict(list)
|
|
spec_decoding_stats: SpecDecodingStats | None = None
|
|
kv_connector_stats: KVConnectorStats | None = (
|
|
kv_connector_output.kv_connector_stats if kv_connector_output else None
|
|
)
|
|
if kv_connector_stats and self.connector:
|
|
kv_stats = self.connector.get_kv_connector_stats()
|
|
if kv_stats:
|
|
kv_connector_stats = kv_connector_stats.aggregate(kv_stats)
|
|
|
|
failed_kv_load_req_ids = None
|
|
if kv_connector_output and kv_connector_output.invalid_block_ids:
|
|
# These blocks contain externally computed tokens that failed to
|
|
# load. Identify affected requests and adjust their computed token
|
|
# count to trigger recomputation of the invalid blocks.
|
|
failed_kv_load_req_ids = self._handle_invalid_blocks(kv_connector_output.invalid_block_ids)
|
|
|
|
# return recomputed requests as EngineCoreOutput
|
|
if scheduler_output.recomputed_reqs is not None:
|
|
for req_info in scheduler_output.recomputed_reqs:
|
|
outputs[req_info.client_index].append(
|
|
EngineCoreOutput(
|
|
request_id=req_info.request_id,
|
|
finish_reason=FinishReason.STOP,
|
|
new_token_ids=[],
|
|
stop_reason="recomputed",
|
|
)
|
|
)
|
|
|
|
# NOTE(woosuk): As len(num_scheduled_tokens) can be up to 1K or more,
|
|
# the below loop can be a performance bottleneck. We should do our best
|
|
# to avoid expensive operations inside the loop.
|
|
stopped_running_reqs: set[Request] = set()
|
|
stopped_preempted_reqs: set[Request] = set()
|
|
for req_id, num_tokens_scheduled in num_scheduled_tokens.items():
|
|
assert num_tokens_scheduled > 0
|
|
if failed_kv_load_req_ids and req_id in failed_kv_load_req_ids:
|
|
# skip failed or rescheduled requests from KV load failure
|
|
continue
|
|
request = self.requests.get(req_id)
|
|
if request is None or request.is_finished():
|
|
# The request is already finished. This can happen if the
|
|
# request is aborted while the model is executing it (e.g.,
|
|
# in pipeline parallelism or in async scheduling).
|
|
# NOTE(Kuntai): When delay_free_blocks=True (for async KV
|
|
# cache transfer in KV connector), the aborted request will not
|
|
# be set to None (in order to finish async KV transfer).
|
|
# In this case, we use is_finished() to check.
|
|
continue
|
|
|
|
req_index = model_runner_output.req_id_to_index[req_id]
|
|
generated_token_ids = sampled_token_ids[req_index] if sampled_token_ids else []
|
|
|
|
scheduled_spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(req_id)
|
|
if scheduled_spec_token_ids and generated_token_ids:
|
|
num_draft_tokens = len(scheduled_spec_token_ids)
|
|
num_accepted = len(generated_token_ids) - 1
|
|
num_rejected = num_draft_tokens - num_accepted
|
|
# num_computed_tokens represents the number of tokens
|
|
# processed in the current step, considering scheduled
|
|
# tokens and rejections. If some tokens are rejected,
|
|
# num_computed_tokens is decreased by the number of rejected
|
|
# tokens.
|
|
if request.num_computed_tokens > 0:
|
|
request.num_computed_tokens -= num_rejected
|
|
# If async scheduling, num_output_placeholders also includes
|
|
# the scheduled spec tokens count and so is similarly adjusted.
|
|
if request.num_output_placeholders > 0:
|
|
request.num_output_placeholders -= num_rejected
|
|
spec_decoding_stats = self.make_spec_decoding_stats(
|
|
spec_decoding_stats,
|
|
num_draft_tokens=num_draft_tokens,
|
|
num_accepted_tokens=num_accepted,
|
|
num_invalid_spec_tokens=scheduler_output.num_invalid_spec_tokens,
|
|
request_id=req_id,
|
|
)
|
|
|
|
stopped = False
|
|
new_logprobs = None
|
|
new_token_ids = generated_token_ids
|
|
pooler_output = pooler_outputs[req_index] if pooler_outputs else None
|
|
kv_transfer_params = None
|
|
status_before_stop = request.status
|
|
|
|
# Check for stop and update request status.
|
|
if new_token_ids:
|
|
new_token_ids, stopped = self._update_request_with_output(request, new_token_ids)
|
|
elif request.pooling_params and pooler_output is not None:
|
|
# Pooling stops as soon as there is output.
|
|
request.status = RequestStatus.FINISHED_STOPPED
|
|
stopped = True
|
|
|
|
routed_experts = None
|
|
finish_reason = None
|
|
if stopped:
|
|
routed_experts = self._get_routed_experts(request)
|
|
|
|
# Capture finish_reason BEFORE _handle_stopped_request, which may
|
|
# reset the status to WAITING for streaming requests that continue.
|
|
finish_reason = request.get_finished_reason()
|
|
finished = self._handle_stopped_request(request)
|
|
if finished:
|
|
kv_transfer_params = self._free_request(request)
|
|
|
|
if status_before_stop == RequestStatus.RUNNING:
|
|
stopped_running_reqs.add(request)
|
|
else:
|
|
stopped_preempted_reqs.add(request)
|
|
|
|
# Extract sample logprobs if needed.
|
|
if request.sampling_params is not None and request.sampling_params.logprobs is not None and logprobs:
|
|
new_logprobs = logprobs.slice_request(req_index, len(new_token_ids))
|
|
|
|
if new_token_ids and self.structured_output_manager.should_advance(request):
|
|
struct_output_request = request.structured_output_request
|
|
assert struct_output_request is not None
|
|
assert struct_output_request.grammar is not None
|
|
ok = struct_output_request.grammar.accept_tokens(req_id, new_token_ids)
|
|
if not ok:
|
|
logger.warning(
|
|
"Unexpected: grammar rejected tokens %s for request %s.",
|
|
new_token_ids,
|
|
req_id,
|
|
)
|
|
|
|
if num_nans_in_logits is not None and req_id in num_nans_in_logits:
|
|
request.num_nans_in_logits = num_nans_in_logits[req_id]
|
|
|
|
# Get prompt logprobs for this request.
|
|
prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
|
|
if new_token_ids or pooler_output is not None or kv_transfer_params or stopped:
|
|
# Add EngineCoreOutput for this Request.
|
|
outputs[request.client_index].append(
|
|
EngineCoreOutput(
|
|
request_id=req_id,
|
|
new_token_ids=new_token_ids,
|
|
finish_reason=finish_reason,
|
|
new_logprobs=new_logprobs,
|
|
new_prompt_logprobs_tensors=prompt_logprobs_tensors,
|
|
pooling_output=pooler_output,
|
|
stop_reason=request.stop_reason,
|
|
events=request.take_events(),
|
|
kv_transfer_params=kv_transfer_params,
|
|
trace_headers=request.trace_headers,
|
|
num_cached_tokens=request.num_cached_tokens,
|
|
num_external_computed_tokens=request.num_external_computed_tokens,
|
|
routed_experts=routed_experts,
|
|
num_nans_in_logits=request.num_nans_in_logits,
|
|
)
|
|
)
|
|
else:
|
|
# Invariant: EngineCore returns no partial prefill outputs.
|
|
assert not prompt_logprobs_tensors
|
|
|
|
# Remove the stopped requests from the running and waiting queues.
|
|
if stopped_running_reqs:
|
|
self.running = remove_all(self.running, stopped_running_reqs)
|
|
if stopped_preempted_reqs:
|
|
# This is a rare case and unlikely to impact performance.
|
|
self.waiting.remove_requests(stopped_preempted_reqs)
|
|
|
|
if failed_kv_load_req_ids and not self.recompute_kv_load_failures:
|
|
requests = [self.requests[req_id] for req_id in failed_kv_load_req_ids]
|
|
self.finish_requests(failed_kv_load_req_ids, RequestStatus.FINISHED_ERROR)
|
|
for request in requests:
|
|
outputs[request.client_index].append(
|
|
EngineCoreOutput(
|
|
request_id=request.request_id,
|
|
new_token_ids=[],
|
|
finish_reason=request.get_finished_reason(),
|
|
events=request.take_events(),
|
|
trace_headers=request.trace_headers,
|
|
num_cached_tokens=request.num_cached_tokens,
|
|
)
|
|
)
|
|
|
|
# KV Connector: update state for finished KV Transfers.
|
|
if kv_connector_output:
|
|
self._update_from_kv_xfer_finished(kv_connector_output)
|
|
|
|
# collect KV cache events from KV cache manager
|
|
events = self.kv_cache_manager.take_events()
|
|
|
|
# collect KV cache events from connector
|
|
if self.connector is not None:
|
|
connector_events = self.connector.take_events()
|
|
if connector_events:
|
|
if events is None:
|
|
events = list(connector_events)
|
|
else:
|
|
events.extend(connector_events)
|
|
|
|
# publish collected KV cache events
|
|
if events:
|
|
batch = KVEventBatch(ts=time.time(), events=events)
|
|
self.kv_event_publisher.publish(batch)
|
|
|
|
# Create EngineCoreOutputs for all clients that have requests with
|
|
# outputs in this step.
|
|
engine_core_outputs = {client_index: EngineCoreOutputs(outputs=outs) for client_index, outs in outputs.items()}
|
|
|
|
finished_req_ids = self.finished_req_ids_dict
|
|
if finished_req_ids:
|
|
# Include ids of requests that finished since last outputs
|
|
# were sent.
|
|
for client_index, finished_set in finished_req_ids.items():
|
|
# Set finished request set in EngineCoreOutputs for this client.
|
|
if (eco := engine_core_outputs.get(client_index)) is not None:
|
|
eco.finished_requests = finished_set
|
|
else:
|
|
engine_core_outputs[client_index] = EngineCoreOutputs(finished_requests=finished_set)
|
|
finished_req_ids.clear()
|
|
|
|
if (stats := self.make_stats(spec_decoding_stats, kv_connector_stats, cudagraph_stats, perf_stats)) is not None:
|
|
# Return stats to only one of the front-ends.
|
|
if (eco := next(iter(engine_core_outputs.values()), None)) is None:
|
|
# We must return the stats even if there are no request
|
|
# outputs this step.
|
|
engine_core_outputs[0] = eco = EngineCoreOutputs()
|
|
eco.scheduler_stats = stats
|
|
|
|
return engine_core_outputs
|
|
|
|
|
|
class AsyncRecomputeScheduler(AsyncScheduler, RecomputeScheduler):
|
|
def __init__(self, *args, **kwargs):
|
|
register_ascend_mla_spec_in_manager()
|
|
|
|
super().__init__(*args, **kwargs)
|